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buchmann |
1.1 |
#include <iostream>
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#include <vector>
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#include <sys/stat.h>
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#include <TCut.h>
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#include <TROOT.h>
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#include <TCanvas.h>
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#include <TMath.h>
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#include <TColor.h>
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#include <TPaveText.h>
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#include <TRandom.h>
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#include <TH1.h>
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#include <TH2.h>
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#include <TF1.h>
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#include <TSQLResult.h>
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#include <TProfile.h>
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//#include "TTbar_stuff.C"
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using namespace std;
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using namespace PlottingSetup;
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void rediscover_the_top(string mcjzb, string datajzb) {
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buchmann |
1.3 |
dout << "Hi! today we are going to (try to) rediscover the top!" << endl;
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buchmann |
1.1 |
TCanvas *c3 = new TCanvas("c3","c3");
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c3->SetLogy(1);
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vector<float> binning;
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//binning=allsamples.get_optimal_binsize(mcjzb,cutmass&&cutOSSF&&cutnJets,20,50,800);
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/*
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binning.push_back(50);
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binning.push_back(100);
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binning.push_back(150);
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binning.push_back(200);
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binning.push_back(500);
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TH1F *dataprediction = allsamples.Draw("dataprediction", "-"+datajzb, binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data, luminosity);
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TH1F *puresignal = allsamples.Draw("puresignal", datajzb, binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data, luminosity);
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// TH1F *puresignal = allsamples.Draw("puresignal", mcjzb, binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,mc, luminosity,allsamples.FindSample("TTJets"));
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TH1F *observed = allsamples.Draw("observed", datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
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/*
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ofstream myfile;
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TH1F *ratio = (TH1F*)observed->Clone();
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ratio->Divide(dataprediction);
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ratio->GetYaxis()->SetTitle("Ratio obs/pred");
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ratio->Draw();
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c3->SaveAs("testratio.png");
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myfile.open ("ShapeFit_log.txt");
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establish_upper_limits(observed,dataprediction,puresignal,"LM4",myfile);
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myfile.close();
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*/
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int nbins=100;
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float low=0;
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float hi=500;
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TCanvas *c4 = new TCanvas("c4","c4",900,900);
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c4->Divide(2,2);
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c4->cd(1);
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c4->cd(1)->SetLogy(1);
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TH1F *datapredictiont = allsamples.Draw("datapredictiont", "-"+datajzb, nbins,low,hi, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data, luminosity);
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TH1F *datapredictiono = allsamples.Draw("datapredictiono", "-"+datajzb, nbins,low,hi, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data, luminosity);
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datapredictiont->Add(datapredictiono,-1);
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buchmann |
1.3 |
dout << "Second way of doing this !!!! Analytical shape to the left :-D" << endl;
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buchmann |
1.1 |
vector<TF1*> functions = do_cb_fit_to_plot(datapredictiont,10);
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datapredictiont->SetMarkerColor(kRed);
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datapredictiont->SetLineColor(kRed);
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datapredictiont->Draw();
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functions[1]->Draw("same");
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TText *title1 = write_title("Top Background Prediction (JZB<0, with osof subtr)");
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title1->Draw();
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c4->cd(2);
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c4->cd(2)->SetLogy(1);
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TH1F *observedt = allsamples.Draw("observedt", datajzb, nbins,low,hi, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data, luminosity);
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observedt->Draw();
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datapredictiont->Draw("histo,same");
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functions[1]->Draw("same");
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TText *title2 = write_title("Observed and predicted background");
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title2->Draw();
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c4->cd(3);
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c4->cd(3)->SetLogy(1);
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// TH1F *ratio = (TH1F*)observedt->Clone();
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TH1F *analytical_background_prediction= new TH1F("analytical_background_prediction","",nbins,low,hi);
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for(int i=0;i<=nbins;i++) {
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analytical_background_prediction->SetBinContent(i+1,functions[1]->Eval(((hi-low)/((float)nbins))*(i+0.5)));
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analytical_background_prediction->SetBinError(i+1,TMath::Sqrt(functions[1]->Eval(((hi-low)/((float)nbins))*(i+0.5))));
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}
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analytical_background_prediction->GetYaxis()->SetTitle("JZB [GeV]");
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analytical_background_prediction->GetYaxis()->CenterTitle();
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TH1F *analyticaldrawonly = (TH1F*)analytical_background_prediction->Clone();
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analytical_background_prediction->SetFillColor(TColor::GetColor("#3399FF"));
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analytical_background_prediction->SetMarkerSize(0);
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analytical_background_prediction->Draw("e5");
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analyticaldrawonly->Draw("histo,same");
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functions[1]->Draw("same");
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TText *title3 = write_title("Analytical bg pred histo");
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title3->Draw();
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c4->cd(4);
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// c4->cd(4)->SetLogy(1);
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vector<float> ratio_binning;
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ratio_binning.push_back(0);
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ratio_binning.push_back(5);
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ratio_binning.push_back(10);
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ratio_binning.push_back(20);
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ratio_binning.push_back(50);
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// ratio_binning.push_back(60);
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/*
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ratio_binning.push_back(51);
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ratio_binning.push_back(52);
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ratio_binning.push_back(53);
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ratio_binning.push_back(54);
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ratio_binning.push_back(55);
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ratio_binning.push_back(56);
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ratio_binning.push_back(57);
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ratio_binning.push_back(58);
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ratio_binning.push_back(59);
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ratio_binning.push_back(60);
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// ratio_binning.push_back(70);*/
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// ratio_binning.push_back(80);
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// ratio_binning.push_back(90);
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ratio_binning.push_back(80);
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// ratio_binning.push_back(110);
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ratio_binning.push_back(500);
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TH1F *observedtb = allsamples.Draw("observedtb", datajzb, ratio_binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data, luminosity);
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TH1F *datapredictiontb = allsamples.Draw("datapredictiontb", "-"+datajzb, ratio_binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data, luminosity);
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TH1F *datapredictiontbo = allsamples.Draw("datapredictiontbo", "-"+datajzb, ratio_binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data, luminosity);
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datapredictiontb->Add(datapredictiontbo,-1);
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TH1F *analytical_background_predictionb = allsamples.Draw("analytical_background_predictionb",datajzb, ratio_binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets&&"mll<2",data, luminosity);
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for(int i=0;i<=ratio_binning.size();i++) {
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analytical_background_predictionb->SetBinContent(i+1,functions[1]->Eval(analytical_background_predictionb->GetBinCenter(i)));
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analytical_background_predictionb->SetBinError(i+1,TMath::Sqrt(functions[1]->Eval(analytical_background_predictionb->GetBinCenter(i))));
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}
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TH1F *ratio = (TH1F*) observedtb->Clone();
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ratio->Divide(datapredictiontb);
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for (int i=0;i<=ratio_binning.size();i++) {
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buchmann |
1.3 |
dout << observedtb->GetBinLowEdge(i+1) << ";"<<observedtb->GetBinContent(i+1) << ";" << datapredictiontb->GetBinContent(i+1) << " --> " << ratio->GetBinContent(i+1) << "+/-" << ratio->GetBinError(i+1) << endl;
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buchmann |
1.1 |
}
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// ratio->Divide(datapredictiontb);
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// ratio->Divide(analytical_background_predictionb);
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// TGraphAsymmErrors *JZBratio= histRatio(observedtb,analytical_background_predictionb,data,ratio_binning);
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// ratio->Divide(analytical_background_prediction);
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// ratio->Divide(datapredictiont);
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// ratio->GetYaxis()->SetTitle("obs/pred");
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// JZBratio->Draw("AP");
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ratio->GetYaxis()->SetRangeUser(0,10);
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ratio->Draw();
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//analytical_background_predictionb->Draw();
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// JZBratio->SetTitle("");
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TText *title4 = write_title("Ratio of observed to predicted");
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title4->Draw();
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// CompleteSave(c4,"test/ttbar_discovery_dataprediction___analytical_function");
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CompleteSave(c4,"test/ttbar_discovery_dataprediction__analytical__new_binning_one_huge_bin_from_80");
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}
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buchmann |
1.2 |
vector<float> compute_one_upper_limit(float mceff,float mcefferr, int ibin, string mcjzb, bool doobserved=false) {
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float sigma95=0.0,sigma95A=0.0;
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buchmann |
1.4 |
int nuisancemodel=1;
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dout << "Now calling : CL95(" << luminosity << "," << lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << Nobs[ibin] << "," << false << "," << nuisancemodel<< ") " << endl;
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sigma95 = CL95(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], Nobs[ibin], false, nuisancemodel);
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buchmann |
1.2 |
if(doobserved) {
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buchmann |
1.4 |
dout << "Now calling : CLA(" << luminosity << "," << lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << nuisancemodel<< ") " << endl;
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sigma95A = CLA(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], nuisancemodel);
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buchmann |
1.2 |
}
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vector<float> sigmas;
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sigmas.push_back(sigma95);
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sigmas.push_back(sigma95A);
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return sigmas;
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}
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void compute_upper_limits_from_counting_experiment(vector<vector<float> > uncertainties,vector<float> jzbcuts, string mcjzb, bool doobserved) {
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buchmann |
1.3 |
dout << "Doing counting experiment ... " << endl;
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| 187 |
buchmann |
1.2 |
vector<vector<string> > limits;
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| 188 |
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vector<vector<float> > vlimits;
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| 189 |
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| 190 |
buchmann |
1.1 |
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| 191 |
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for(int isample=0;isample<signalsamples.collection.size();isample++) {
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| 192 |
buchmann |
1.2 |
vector<string> rows;
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vector<float> vrows;
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buchmann |
1.3 |
dout << "Considering sample " << signalsamples.collection[isample].samplename << endl;
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buchmann |
1.2 |
rows.push_back(signalsamples.collection[isample].samplename);
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buchmann |
1.1 |
for(int ibin=0;ibin<jzbcuts.size();ibin++) {
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| 197 |
buchmann |
1.3 |
dout << "_________________________________________________________________________________" << endl;
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| 198 |
buchmann |
1.2 |
float JZBcutat=uncertainties[isample*jzbcuts.size()+ibin][0];
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float mceff=uncertainties[isample*jzbcuts.size()+ibin][1];
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| 200 |
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float staterr=uncertainties[isample*jzbcuts.size()+ibin][2];
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| 201 |
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float systerr=uncertainties[isample*jzbcuts.size()+ibin][3];
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| 202 |
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float toterr =uncertainties[isample*jzbcuts.size()+ibin][4];
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| 203 |
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float observed,null,result;
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| 204 |
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fill_result_histos(observed, null,null,null,null,null,null,null,mcjzb,JZBcutat,(int)5,result,(signalsamples.FindSample(signalsamples.collection[isample].filename)),signalsamples);
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observed-=result;//this is the actual excess we see!
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| 206 |
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float expected=observed/luminosity;
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| 207 |
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| 208 |
buchmann |
1.3 |
dout << "Sample: " << signalsamples.collection[isample].samplename << ", JZB>"<<JZBcutat<< " : " << mceff << " +/- " << staterr << " (stat) +/- " << systerr << " (syst) --> toterr = " << toterr << endl;
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| 209 |
buchmann |
1.2 |
vector<float> sigmas = compute_one_upper_limit(mceff,toterr,ibin,mcjzb,doobserved);
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| 210 |
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| 211 |
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if(doobserved) {
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rows.push_back(any2string(sigmas[0])+";"+any2string(sigmas[1])+";"+"("+any2string(expected)+")");
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vrows.push_back(sigmas[0]);
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vrows.push_back(sigmas[1]);
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vrows.push_back(expected);
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}
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else {
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rows.push_back(any2string(sigmas[0])+"("+any2string(expected)+")");
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vrows.push_back(sigmas[0]);
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| 220 |
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vrows.push_back(expected);
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| 221 |
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}
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| 222 |
buchmann |
1.1 |
}//end of bin loop
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| 223 |
buchmann |
1.2 |
limits.push_back(rows);
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| 224 |
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vlimits.push_back(vrows);
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| 225 |
buchmann |
1.1 |
}//end of sample loop
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| 226 |
buchmann |
1.3 |
dout << endl << endl << "PAS table 3: " << endl << endl;
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| 227 |
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dout << "\t";
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| 228 |
buchmann |
1.2 |
for (int irow=0;irow<jzbcuts.size();irow++) {
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| 229 |
buchmann |
1.3 |
dout << jzbcuts[irow] << "\t";
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| 230 |
buchmann |
1.2 |
}
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| 231 |
buchmann |
1.3 |
dout << endl;
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| 232 |
buchmann |
1.2 |
for(int irow=0;irow<limits.size();irow++) {
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| 233 |
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for(int ientry=0;ientry<limits[irow].size();ientry++) {
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| 234 |
buchmann |
1.3 |
dout << limits[irow][ientry] << "\t";
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| 235 |
buchmann |
1.2 |
}
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| 236 |
buchmann |
1.3 |
dout << endl;
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| 237 |
buchmann |
1.2 |
}
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| 238 |
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| 239 |
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if(!doobserved) {
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| 240 |
buchmann |
1.3 |
dout << endl << endl << "LIMITS: " << endl;
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| 241 |
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dout << "\t";
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| 242 |
buchmann |
1.2 |
for (int irow=0;irow<jzbcuts.size();irow++) {
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| 243 |
buchmann |
1.3 |
dout << jzbcuts[irow] << "\t";
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| 244 |
buchmann |
1.2 |
}
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| 245 |
buchmann |
1.3 |
dout << endl;
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| 246 |
buchmann |
1.2 |
for(int irow=0;irow<limits.size();irow++) {
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| 247 |
buchmann |
1.3 |
dout << limits[irow][0] << "\t";
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| 248 |
buchmann |
1.2 |
for(int ientry=0;ientry<jzbcuts.size();ientry++) {
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| 249 |
buchmann |
1.3 |
dout << Round(vlimits[irow][2*ientry] / vlimits[irow][2*ientry+1],3)<< "\t";
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| 250 |
buchmann |
1.2 |
}
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| 251 |
buchmann |
1.3 |
dout << endl;
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| 252 |
buchmann |
1.2 |
}
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| 253 |
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}//do observed
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| 254 |
buchmann |
1.3 |
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| 255 |
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dout << endl << endl << "Final selection efficiencies with total statistical and systematic errors, and corresponding observed and expected upper limits (UL) on ($\\sigma\\times$ BR $\\times$ acceptance) for the LM4 and LM8 scenarios, in the different regions. The last column contains the predicted ($\\sigma \\times $BR$\\times$ acceptance) at NLO obtained from Monte Carlo simulation." << endl;
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| 256 |
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dout << "Scenario \t Efficiency [%] \t Upper limits [pb] \t Prediction [pb]" << endl;
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| 257 |
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for(int icut=0;icut<jzbcuts.size();icut++) {
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| 258 |
|
|
dout << "Region with JZB>" << jzbcuts[icut] << endl;
|
| 259 |
|
|
for(int isample=0;isample<signalsamples.collection.size();isample++) {
|
| 260 |
|
|
dout << limits[icut][0] << "\t" << Round(100*uncertainties[isample*jzbcuts.size()+icut][1],1) << "+/-" << Round(100*uncertainties[isample*jzbcuts.size()+icut][2],1) << " (stat) +/- " << Round(100*uncertainties[isample*jzbcuts.size()+icut][3],1) << " (syst) \t" << Round((vlimits[isample][2*icut]),3) << "\t" << Round(vlimits[isample][2*icut+1],3) << endl;
|
| 261 |
|
|
}
|
| 262 |
|
|
dout << endl;
|
| 263 |
|
|
}
|
| 264 |
buchmann |
1.4 |
|
| 265 |
|
|
write_warning("compute_upper_limits_from_counting_experiment","Still need to update the script");
|
| 266 |
buchmann |
1.1 |
}
|
| 267 |
|
|
|
| 268 |
|
|
void susy_scan_axis_labeling(TH2F *histo) {
|
| 269 |
|
|
histo->GetXaxis()->SetTitle("#Chi_{2}^{0}-LSP");
|
| 270 |
|
|
histo->GetXaxis()->CenterTitle();
|
| 271 |
|
|
histo->GetYaxis()->SetTitle("m_{#tilde{q}}");
|
| 272 |
|
|
histo->GetYaxis()->CenterTitle();
|
| 273 |
|
|
}
|
| 274 |
|
|
|
| 275 |
|
|
void scan_susy_space(string mcjzb, string datajzb) {
|
| 276 |
|
|
TCanvas *c3 = new TCanvas("c3","c3");
|
| 277 |
|
|
vector<float> binning;
|
| 278 |
|
|
binning=allsamples.get_optimal_binsize(mcjzb,cutmass&&cutOSSF&&cutnJets,20,50,800);
|
| 279 |
|
|
float arrbinning[binning.size()];
|
| 280 |
|
|
for(int i=0;i<binning.size();i++) arrbinning[i]=binning[i];
|
| 281 |
|
|
TH1F *puredata = allsamples.Draw("puredata", datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
|
| 282 |
|
|
puredata->SetMarkerSize(DataMarkerSize);
|
| 283 |
|
|
TH1F *allbgs = allsamples.Draw("allbgs", "-"+datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
|
| 284 |
|
|
TH1F *allbgsb = allsamples.Draw("allbgsb", "-"+datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data,luminosity);
|
| 285 |
|
|
TH1F *allbgsc = allsamples.Draw("allbgsc", datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data,luminosity);
|
| 286 |
|
|
allbgs->Add(allbgsb,-1);
|
| 287 |
|
|
allbgs->Add(allbgsc);
|
| 288 |
|
|
int ndata=puredata->Integral();
|
| 289 |
|
|
ofstream myfile;
|
| 290 |
|
|
myfile.open ("susyscan_log.txt");
|
| 291 |
|
|
TFile *susyscanfile = new TFile("/scratch/fronga/SMS/T5z_SqSqToQZQZ_38xFall10.root");
|
| 292 |
|
|
TTree *suevents = (TTree*)susyscanfile->Get("events");
|
| 293 |
|
|
TH2F *exclusionmap = new TH2F("exclusionmap","",20,0,500,20,0,1000);
|
| 294 |
|
|
TH2F *exclusionmap1s = new TH2F("exclusionmap1s","",20,0,500,20,0,1000);
|
| 295 |
|
|
TH2F *exclusionmap2s = new TH2F("exclusionmap2s","",20,0,500,20,0,1000);
|
| 296 |
|
|
TH2F *exclusionmap3s = new TH2F("exclusionmap3s","",20,0,500,20,0,1000);
|
| 297 |
|
|
|
| 298 |
|
|
susy_scan_axis_labeling(exclusionmap);
|
| 299 |
|
|
susy_scan_axis_labeling(exclusionmap1s);
|
| 300 |
|
|
susy_scan_axis_labeling(exclusionmap2s);
|
| 301 |
|
|
susy_scan_axis_labeling(exclusionmap3s);
|
| 302 |
|
|
|
| 303 |
|
|
Int_t MyPalette[100];
|
| 304 |
|
|
Double_t r[] = {0., 0.0, 1.0, 1.0, 1.0};
|
| 305 |
|
|
Double_t g[] = {0., 0.0, 0.0, 1.0, 1.0};
|
| 306 |
|
|
Double_t b[] = {0., 1.0, 0.0, 0.0, 1.0};
|
| 307 |
|
|
Double_t stop[] = {0., .25, .50, .75, 1.0};
|
| 308 |
|
|
Int_t FI = TColor::CreateGradientColorTable(5, stop, r, g, b, 100);
|
| 309 |
|
|
for (int i=0;i<100;i++) MyPalette[i] = FI+i;
|
| 310 |
|
|
|
| 311 |
|
|
gStyle->SetPalette(100, MyPalette);
|
| 312 |
|
|
|
| 313 |
|
|
for(int m23=50;m23<500;m23+=25) {
|
| 314 |
|
|
for (int m0=(2*(m23-50)+150);m0<=1000;m0+=50)
|
| 315 |
|
|
{
|
| 316 |
|
|
c3->cd();
|
| 317 |
|
|
stringstream drawcondition;
|
| 318 |
|
|
drawcondition << "pfJetGoodNum>=3&&(TMath::Abs(masses[0]-"<<m0<<")<10&&TMath::Abs(masses[2]-masses[3]-"<<m23<<")<10)&&mll>5&&id1==id2";
|
| 319 |
|
|
TH1F *puresignal = new TH1F("puresignal","puresignal",binning.size()-1,arrbinning);
|
| 320 |
|
|
TH1F *puresignall= new TH1F("puresignall","puresignal",binning.size()-1,arrbinning);
|
| 321 |
|
|
stringstream drawvar,drawvar2;
|
| 322 |
|
|
drawvar<<mcjzb<<">>puresignal";
|
| 323 |
|
|
drawvar2<<"-"<<mcjzb<<">>puresignall";
|
| 324 |
|
|
suevents->Draw(drawvar.str().c_str(),drawcondition.str().c_str());
|
| 325 |
|
|
suevents->Draw(drawvar2.str().c_str(),drawcondition.str().c_str());
|
| 326 |
|
|
if(puresignal->Integral()<60) {
|
| 327 |
|
|
delete puresignal;
|
| 328 |
|
|
continue;
|
| 329 |
|
|
}
|
| 330 |
|
|
puresignal->Add(puresignall,-1);//we need to correct for the signal contamination - we effectively only see (JZB>0)-(JZB<0) !!
|
| 331 |
|
|
puresignal->Scale(ndata/(20*puresignal->Integral()));//normalizing it to 5% of the data
|
| 332 |
|
|
stringstream saveas;
|
| 333 |
|
|
saveas<<"Model_Scan/m0_"<<m0<<"__m23_"<<m23;
|
| 334 |
buchmann |
1.3 |
dout << "PLEASE KEEP IN MIND THAT SIGNAL CONTAMINATION IS NOT REALLY TAKEN CARE OF YET DUE TO LOW STATISTICS! SHOULD BE SOMETHING LIKE THIS : "<< endl;
|
| 335 |
buchmann |
1.1 |
// TH1F *signalpredlo = allsamples.Draw("signalpredlo", "-"+mcjzb, binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc, luminosity,allsamples.FindSample("LM4"));
|
| 336 |
|
|
// TH1F *signalpredro = allsamples.Draw("signalpredro", mcjzb, binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc, luminosity,allsamples.FindSample("LM4"));
|
| 337 |
|
|
// TH1F *puredata = allsamples.Draw("puredata", datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
|
| 338 |
|
|
// signalpred->Add(signalpredlo,-1);
|
| 339 |
|
|
// signalpred->Add(signalpredro);
|
| 340 |
|
|
// puresignal->Add(signalpred,-1);//subtracting signal contamination
|
| 341 |
|
|
//---------------------
|
| 342 |
buchmann |
1.3 |
// dout << "(m0,m23)=("<<m0<<","<<m23<<") contains " << puresignal->Integral() << endl;
|
| 343 |
buchmann |
1.1 |
// TH1F *puresignal = allsamples.Draw("puresignal",mcjzb, binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,mc, luminosity,allsamples.FindSample("LM4"));
|
| 344 |
|
|
vector<float> results=establish_upper_limits(puredata,allbgs,puresignal,saveas.str(),myfile);
|
| 345 |
|
|
if(results.size()==0) {
|
| 346 |
|
|
delete puresignal;
|
| 347 |
|
|
continue;
|
| 348 |
|
|
}
|
| 349 |
|
|
exclusionmap->Fill(m23,m0,results[0]);
|
| 350 |
|
|
exclusionmap1s->Fill(m23,m0,results[1]);
|
| 351 |
|
|
exclusionmap2s->Fill(m23,m0,results[2]);
|
| 352 |
|
|
exclusionmap3s->Fill(m23,m0,results[3]);
|
| 353 |
|
|
delete puresignal;
|
| 354 |
buchmann |
1.3 |
dout << "(m0,m23)=("<<m0<<","<<m23<<") : 3 sigma at " << results[3] << endl;
|
| 355 |
buchmann |
1.1 |
}
|
| 356 |
|
|
}//end of model scan for loop
|
| 357 |
|
|
|
| 358 |
buchmann |
1.3 |
dout << "Exclusion Map contains" << exclusionmap->Integral() << " (integral) and entries: " << exclusionmap->GetEntries() << endl;
|
| 359 |
buchmann |
1.1 |
c3->cd();
|
| 360 |
|
|
exclusionmap->Draw("CONTZ");
|
| 361 |
|
|
CompleteSave(c3,"Model_Scan/CONT/Model_Scan_Mean_values");
|
| 362 |
|
|
exclusionmap->Draw("COLZ");
|
| 363 |
|
|
CompleteSave(c3,"Model_Scan/COL/Model_Scan_Mean_values");
|
| 364 |
|
|
|
| 365 |
|
|
exclusionmap1s->Draw("CONTZ");
|
| 366 |
|
|
CompleteSave(c3,"Model_Scan/CONT/Model_Scan_1sigma_values");
|
| 367 |
|
|
exclusionmap1s->Draw("COLZ");
|
| 368 |
|
|
CompleteSave(c3,"Model_Scan/COL/Model_Scan_1sigma_values");
|
| 369 |
|
|
|
| 370 |
|
|
exclusionmap2s->Draw("CONTZ");
|
| 371 |
|
|
CompleteSave(c3,"Model_Scan/CONT/Model_Scan_2sigma_values");
|
| 372 |
|
|
exclusionmap2s->Draw("COLZ");
|
| 373 |
|
|
CompleteSave(c3,"Model_Scan/COL/Model_Scan_2sigma_values");
|
| 374 |
|
|
|
| 375 |
|
|
exclusionmap3s->Draw("CONTZ");
|
| 376 |
|
|
CompleteSave(c3,"Model_Scan/CONT/Model_Scan_3sigma_values");
|
| 377 |
|
|
exclusionmap3s->Draw("COLZ");
|
| 378 |
|
|
CompleteSave(c3,"Model_Scan/COL/Model_Scan_3sigma_values");
|
| 379 |
|
|
|
| 380 |
|
|
TFile *exclusion_limits = new TFile("exclusion_limits.root","RECREATE");
|
| 381 |
|
|
exclusionmap->Write();
|
| 382 |
|
|
exclusionmap1s->Write();
|
| 383 |
|
|
exclusionmap2s->Write();
|
| 384 |
|
|
exclusionmap3s->Write();
|
| 385 |
|
|
exclusion_limits->Close();
|
| 386 |
|
|
susyscanfile->Close();
|
| 387 |
|
|
|
| 388 |
|
|
myfile.close();
|
| 389 |
|
|
}
|
| 390 |
|
|
|
| 391 |
|
|
|
| 392 |
|
|
|
| 393 |
|
|
|
| 394 |
buchmann |
1.5 |
//********************************************************************** new : Limits using SHAPES ***********************************
|
| 395 |
|
|
|
| 396 |
|
|
void limit_shapes_for_systematic_effect(TFile *limfile, string identifier, string mcjzb, string datajzb, int JES,vector<float> binning, TCanvas *limcan) {
|
| 397 |
|
|
dout << "Creatig shape templates ... ";
|
| 398 |
|
|
if(identifier!="") dout << "for systematic called "<<identifier;
|
| 399 |
|
|
dout << endl;
|
| 400 |
|
|
int dataormc=mcwithsignal;//this is only for tests - for real life you want dataormc=data !!!
|
| 401 |
|
|
if(dataormc!=data) write_warning("limit_shapes_for_systematic_effect","WATCH OUT! Not using data for limits!!!! this is ok for tests, but not ok for anything official!");
|
| 402 |
|
|
|
| 403 |
|
|
TCut limitnJetcut;
|
| 404 |
|
|
if(JES==noJES) limitnJetcut=cutnJets;
|
| 405 |
|
|
else {
|
| 406 |
|
|
if(JES==JESdown) limitnJetcut=cutnJetsJESdown;
|
| 407 |
|
|
if(JES==JESup) limitnJetcut=cutnJetsJESup;
|
| 408 |
|
|
}
|
| 409 |
|
|
TH1F *ZOSSFP = allsamples.Draw("ZOSSFP",datajzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,dataormc,luminosity);
|
| 410 |
|
|
TH1F *ZOSOFP = allsamples.Draw("ZOSOFP",datajzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,dataormc,luminosity);
|
| 411 |
|
|
TH1F *ZOSSFN = allsamples.Draw("ZOSSFN","-"+datajzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,dataormc,luminosity);
|
| 412 |
|
|
TH1F *ZOSOFN = allsamples.Draw("ZOSOFN","-"+datajzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,dataormc,luminosity);
|
| 413 |
|
|
|
| 414 |
|
|
TH1F *SBOSSFP = allsamples.Draw("SBOSSFP",datajzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
|
| 415 |
|
|
TH1F *SBOSOFP = allsamples.Draw("SBOSOFP",datajzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
|
| 416 |
|
|
TH1F *SBOSSFN = allsamples.Draw("SBOSSFN","-"+datajzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
|
| 417 |
|
|
TH1F *SBOSOFN = allsamples.Draw("SBOSOFN","-"+datajzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
|
| 418 |
|
|
|
| 419 |
|
|
TH1F *LZOSSFP = allsamples.Draw("LZOSSFP",mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 420 |
|
|
TH1F *LZOSOFP = allsamples.Draw("LZOSOFP",mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 421 |
|
|
TH1F *LZOSSFN = allsamples.Draw("LZOSSFN","-"+mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 422 |
|
|
TH1F *LZOSOFN = allsamples.Draw("LZOSOFN","-"+mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 423 |
|
|
|
| 424 |
|
|
TH1F *LSBOSSFP = allsamples.Draw("LSBOSSFP",mcjzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 425 |
|
|
TH1F *LSBOSOFP = allsamples.Draw("LSBOSOFP",mcjzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 426 |
|
|
TH1F *LSBOSSFN = allsamples.Draw("LSBOSSFN","-"+mcjzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 427 |
|
|
TH1F *LSBOSOFN = allsamples.Draw("LSBOSOFN","-"+mcjzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
|
| 428 |
|
|
|
| 429 |
|
|
string obsname="data_obs";
|
| 430 |
|
|
string predname="background";
|
| 431 |
|
|
string signalname="signal";
|
| 432 |
|
|
if(identifier!="") {
|
| 433 |
|
|
obsname=("data_"+identifier);
|
| 434 |
|
|
predname=("background_"+identifier);
|
| 435 |
|
|
signalname="signal_"+identifier;
|
| 436 |
|
|
}
|
| 437 |
|
|
|
| 438 |
|
|
TH1F *obs = (TH1F*)ZOSSFP->Clone();
|
| 439 |
|
|
obs->SetName(obsname.c_str());
|
| 440 |
|
|
obs->Write();
|
| 441 |
|
|
TH1F *pred = (TH1F*)ZOSSFN->Clone();
|
| 442 |
|
|
pred->Add(ZOSOFP,1.0/3);
|
| 443 |
|
|
pred->Add(ZOSOFN,-1.0/3);
|
| 444 |
|
|
pred->Add(SBOSSFP,1.0/3);
|
| 445 |
|
|
pred->Add(SBOSSFN,-1.0/3);
|
| 446 |
|
|
pred->Add(SBOSOFP,1.0/3);
|
| 447 |
|
|
pred->Add(SBOSOFN,-1.0/3);
|
| 448 |
|
|
pred->SetName(predname.c_str());
|
| 449 |
|
|
pred->Write();
|
| 450 |
|
|
|
| 451 |
|
|
// TH1F *Lobs = (TH1F*)LZOSSFP->Clone();
|
| 452 |
|
|
// TH1F *Lpred = (TH1F*)LZOSSFN->Clone();
|
| 453 |
|
|
|
| 454 |
|
|
TH1F *Lobs = new TH1F("Lobs","Lobs",binning.size()-1,&binning[0]);
|
| 455 |
|
|
TH1F *Lpred = new TH1F("Lpred","Lpred",binning.size()-1,&binning[0]);
|
| 456 |
|
|
Lobs->Add(LZOSSFP);
|
| 457 |
|
|
Lpred->Add(LZOSSFN);
|
| 458 |
|
|
Lpred->Add(LZOSOFP,1.0/3);
|
| 459 |
|
|
Lpred->Add(LZOSOFN,-1.0/3);
|
| 460 |
|
|
Lpred->Add(LSBOSSFP,1.0/3);
|
| 461 |
|
|
Lpred->Add(LSBOSSFN,-1.0/3);
|
| 462 |
|
|
Lpred->Add(LSBOSOFP,1.0/3);
|
| 463 |
|
|
Lpred->Add(LSBOSOFN,-1.0/3);
|
| 464 |
|
|
TH1F *signal = (TH1F*)Lobs->Clone();
|
| 465 |
|
|
signal->Add(Lpred,-1);
|
| 466 |
|
|
signal->SetName(signalname.c_str());
|
| 467 |
|
|
signal->Write();
|
| 468 |
|
|
|
| 469 |
|
|
delete Lobs;
|
| 470 |
|
|
delete Lpred;
|
| 471 |
|
|
|
| 472 |
|
|
delete ZOSSFP;
|
| 473 |
|
|
delete ZOSOFP;
|
| 474 |
|
|
delete ZOSSFN;
|
| 475 |
|
|
delete ZOSOFN;
|
| 476 |
|
|
|
| 477 |
|
|
delete SBOSSFP;
|
| 478 |
|
|
delete SBOSOFP;
|
| 479 |
|
|
delete SBOSSFN;
|
| 480 |
|
|
delete SBOSOFN;
|
| 481 |
|
|
|
| 482 |
|
|
delete LZOSSFP;
|
| 483 |
|
|
delete LZOSOFP;
|
| 484 |
|
|
delete LZOSSFN;
|
| 485 |
|
|
delete LZOSOFN;
|
| 486 |
|
|
|
| 487 |
|
|
delete LSBOSSFP;
|
| 488 |
|
|
delete LSBOSOFP;
|
| 489 |
|
|
delete LSBOSSFN;
|
| 490 |
|
|
delete LSBOSOFN;
|
| 491 |
|
|
|
| 492 |
|
|
}
|
| 493 |
|
|
|
| 494 |
|
|
void prepare_datacard(TFile *f) {
|
| 495 |
|
|
TH1F *dataob = (TH1F*)f->Get("data_obs");
|
| 496 |
|
|
TH1F *signal = (TH1F*)f->Get("signal");
|
| 497 |
|
|
TH1F *background = (TH1F*)f->Get("background");
|
| 498 |
|
|
|
| 499 |
|
|
ofstream datacard;
|
| 500 |
|
|
ensure_directory_exists(get_directory()+"/limits");
|
| 501 |
|
|
datacard.open ((get_directory()+"/limits/susylm4datacard.txt").c_str());
|
| 502 |
|
|
datacard << "Writing this to a file.\n";
|
| 503 |
|
|
datacard << "imax 1\n";
|
| 504 |
|
|
datacard << "jmax 1\n";
|
| 505 |
|
|
datacard << "kmax *\n";
|
| 506 |
|
|
datacard << "---------------\n";
|
| 507 |
|
|
datacard << "shapes * * limitfile.root $PROCESS $PROCESS_$SYSTEMATIC\n";
|
| 508 |
|
|
datacard << "---------------\n";
|
| 509 |
|
|
datacard << "bin 1\n";
|
| 510 |
|
|
datacard << "observation "<<dataob->Integral()<<"\n";
|
| 511 |
|
|
datacard << "------------------------------\n";
|
| 512 |
|
|
datacard << "bin 1 1\n";
|
| 513 |
|
|
datacard << "process signal background\n";
|
| 514 |
|
|
datacard << "process 0 1\n";
|
| 515 |
|
|
datacard << "rate "<<signal->Integral()<<" "<<background->Integral()<<"\n";
|
| 516 |
|
|
datacard << "--------------------------------\n";
|
| 517 |
|
|
datacard << "lumi lnN 1.10 1.0\n";
|
| 518 |
|
|
datacard << "bgnorm lnN 1.00 1.4 uncertainty on our prediction (40%)\n";
|
| 519 |
|
|
datacard << "JES shape 1 1 uncertainty on background shape and normalization\n";
|
| 520 |
|
|
datacard << "peak shape 1 1 uncertainty on signal resolution. Assume the histogram is a 2 sigma shift, \n";
|
| 521 |
|
|
datacard << "# so divide the unit gaussian by 2 before doing the interpolation\n";
|
| 522 |
|
|
datacard.close();
|
| 523 |
|
|
}
|
| 524 |
|
|
|
| 525 |
|
|
|
| 526 |
|
|
void prepare_limits(string mcjzb, string datajzb, float jzbpeakerrordata, float jzbpeakerrormc, vector<float> jzbbins) {
|
| 527 |
|
|
ensure_directory_exists(get_directory()+"/limits");
|
| 528 |
|
|
TFile *limfile = new TFile((get_directory()+"/limits/limitfile.root").c_str(),"RECREATE");
|
| 529 |
|
|
TCanvas *limcan = new TCanvas("limcan","Canvas for calculating limits");
|
| 530 |
|
|
limit_shapes_for_systematic_effect(limfile,"",mcjzb,datajzb,noJES,jzbbins,limcan);
|
| 531 |
|
|
limit_shapes_for_systematic_effect(limfile,"peakUp",newjzbexpression(mcjzb,jzbpeakerrormc),newjzbexpression(datajzb,jzbpeakerrordata),noJES,jzbbins,limcan);
|
| 532 |
|
|
limit_shapes_for_systematic_effect(limfile,"peakDown",newjzbexpression(mcjzb,-jzbpeakerrormc),newjzbexpression(datajzb,-jzbpeakerrordata),noJES,jzbbins,limcan);
|
| 533 |
|
|
limit_shapes_for_systematic_effect(limfile,"JESUp",mcjzb,datajzb,JESup,jzbbins,limcan);
|
| 534 |
|
|
limit_shapes_for_systematic_effect(limfile,"JESDown",mcjzb,datajzb,JESdown,jzbbins,limcan);
|
| 535 |
|
|
|
| 536 |
|
|
prepare_datacard(limfile);
|
| 537 |
|
|
|
| 538 |
|
|
write_info("prepare_limits","limitfile.root and datacard.txt have been generated. You can now use them to calculate limits!");
|
| 539 |
|
|
limfile->Close();
|
| 540 |
|
|
|
| 541 |
|
|
} |
